import os
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Correct model name
MODEL_NAME = "bigcode/starcoder"

# Ensure the token is provided
HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
if not HF_TOKEN:
    raise ValueError("Missing Hugging Face token. Set HUGGINGFACE_TOKEN as an environment variable.")

# Set device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load tokenizer with authentication
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)

# Load model with optimizations
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    token=HF_TOKEN,
    torch_dtype=torch.float16,  # Reduce memory usage
    low_cpu_mem_usage=True,     # Optimize loading
    device_map="auto",         # Automatic device placement
    offload_folder="offload"    # Offload to disk if needed
).to(device)

def generate_code(prompt: str, max_tokens: int = 256):
    """Generates code based on the input prompt."""
    if not prompt.strip():
        return "Error: Empty prompt provided."

    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    output = model.generate(**inputs, max_new_tokens=max_tokens)
    return tokenizer.decode(output[0], skip_special_tokens=True)